## The Question :

*326 people think this question is useful*

I have a Numpy array consisting of a list of lists, representing a two-dimensional array with row labels and column names as shown below:

data = array([['','Col1','Col2'],['Row1',1,2],['Row2',3,4]])

I’d like the resulting DataFrame to have Row1 and Row2 as index values, and Col1, Col2 as header values

I can specify the index as follows:

df = pd.DataFrame(data,index=data[:,0]),

however I am unsure how to best assign column headers.

*The Question Comments :*

## The Answer 1

*361 people think this answer is useful*

You need to specify `data`

, `index`

and `columns`

to `DataFrame`

constructor, as in:

>>> pd.DataFrame(data=data[1:,1:], # values
... index=data[1:,0], # 1st column as index
... columns=data[0,1:]) # 1st row as the column names

**edit**: as in the @joris comment, you may need to change above to `np.int_(data[1:,1:])`

to have correct data type.

## The Answer 2

*113 people think this answer is useful*

Here is an easy to understand solution

import numpy as np
import pandas as pd
# Creating a 2 dimensional numpy array
>>> data = np.array([[5.8, 2.8], [6.0, 2.2]])
>>> print(data)
>>> data
array([[5.8, 2.8],
[6. , 2.2]])
# Creating pandas dataframe from numpy array
>>> dataset = pd.DataFrame({'Column1': data[:, 0], 'Column2': data[:, 1]})
>>> print(dataset)
Column1 Column2
0 5.8 2.8
1 6.0 2.2

## The Answer 3

*25 people think this answer is useful*

I agree with Joris; it seems like you should be doing this differently, like with numpy record arrays. Modifying “option 2” from this great answer, you could do it like this:

import pandas
import numpy
dtype = [('Col1','int32'), ('Col2','float32'), ('Col3','float32')]
values = numpy.zeros(20, dtype=dtype)
index = ['Row'+str(i) for i in range(1, len(values)+1)]
df = pandas.DataFrame(values, index=index)

## The Answer 4

*17 people think this answer is useful*

This can be done simply by using from_records of pandas DataFrame

import numpy as np
import pandas as pd
# Creating a numpy array
x = np.arange(1,10,1).reshape(-1,1)
dataframe = pd.DataFrame.from_records(x)

## The Answer 5

*15 people think this answer is useful*

>>import pandas as pd
>>import numpy as np
>>data.shape
(480,193)
>>type(data)
numpy.ndarray
>>df=pd.DataFrame(data=data[0:,0:],
... index=[i for i in range(data.shape[0])],
... columns=['f'+str(i) for i in range(data.shape[1])])
>>df.head()
[![array to dataframe][1]][1]

## The Answer 6

*8 people think this answer is useful*

Adding to @behzad.nouri ‘s answer – we can create a helper routine to handle this common scenario:

def csvDf(dat,**kwargs):
from numpy import array
data = array(dat)
if data is None or len(data)==0 or len(data[0])==0:
return None
else:
return pd.DataFrame(data[1:,1:],index=data[1:,0],columns=data[0,1:],**kwargs)

Let’s try it out:

data = [['','a','b','c'],['row1','row1cola','row1colb','row1colc'],
['row2','row2cola','row2colb','row2colc'],['row3','row3cola','row3colb','row3colc']]
csvDf(data)
In [61]: csvDf(data)
Out[61]:
a b c
row1 row1cola row1colb row1colc
row2 row2cola row2colb row2colc
row3 row3cola row3colb row3colc

## The Answer 7

*3 people think this answer is useful*

I think this is a simple and intuitive method:

data = np.array([[0, 0], [0, 1] , [1, 0] , [1, 1]])
reward = np.array([1,0,1,0])
dataset = pd.DataFrame()
dataset['StateAttributes'] = data.tolist()
dataset['reward'] = reward.tolist()
dataset

returns:

But there are performance implications detailed here:

How to set the value of a pandas column as list

## The Answer 8

*3 people think this answer is useful*

Here simple example to create pandas dataframe by using numpy array.

import numpy as np
import pandas as pd
# create an array
var1 = np.arange(start=1, stop=21, step=1).reshape(-1)
var2 = np.random.rand(20,1).reshape(-1)
print(var1.shape)
print(var2.shape)
dataset = pd.DataFrame()
dataset['col1'] = var1
dataset['col2'] = var2
dataset.head()

## The Answer 9

*0 people think this answer is useful*

It’s not so short, but maybe can help you.

Creating Array

import numpy as np
import pandas as pd
data = np.array([['col1', 'col2'], [4.8, 2.8], [7.0, 1.2]])
>>> data
array([['col1', 'col2'],
['4.8', '2.8'],
['7.0', '1.2']], dtype='<U4')

Creating data frame

df = pd.DataFrame(i for i in data).transpose()
df.drop(0, axis=1, inplace=True)
df.columns = data[0]
df
>>> df
col1 col2
0 4.8 7.0
1 2.8 1.2